Changes in / [a66b004:b669b49] in sasmodels

Files:

• doc/guide/pd/pd_boltzmann.jpg (deleted)
• doc/guide/pd/pd_uniform.jpg (deleted)
• doc/guide/pd/polydispersity.rst (modified) (4 diffs)
• sasmodels/weights.py (modified) (5 diffs)

doc/guide/pd/polydispersity.rst

@@ -42 +42 @@
 calculations are generally more robust with more data points or more angles.
 
-The following six distribution functions are provided:
+The following five distribution functions are provided:
 
 *  *Rectangular Distribution*
-*  *Uniform Distribution*
 *  *Gaussian Distribution*
 *  *Lognormal Distribution*
 *  *Schulz Distribution*
 *  *Array Distribution*
-*  *Boltzmann Distribution*
 
 These are all implemented as *number-average* distributions.
@@ -87 +85 @@
     Rectangular distribution.
 
-Uniform Distribution
-^^^^^^^^^^^^^^^^^^^^^^^^
-
-The Uniform Distribution is defined as
-
-    .. math::
-
-        f(x) = \frac{1}{\text{Norm}}
-        \begin{cases}
-          1 & \text{for } |x - \bar x| \leq \sigma \\
-          0 & \text{for } |x - \bar x| > \sigma
-        \end{cases}
-
-    where $\bar x$ is the mean of the distribution, $\sigma$ is the half-width, and
-    *Norm* is a normalization factor which is determined during the numerical
-    calculation.
-
-    Note that the polydispersity is given by
-
-    .. math:: \text{PD} = \sigma / \bar x
-
-    .. figure:: pd_uniform.jpg
-
-        Uniform distribution.
-
 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
 
@@ -210 +183 @@
 ^^^^^^^^^^^^^^^^^^
 
-This user-definable distribution should be given as a simple ASCII text
+This user-definable distribution should be given as as a simple ASCII text
 file where the array is defined by two columns of numbers: $x$ and $f(x)$.
 The $f(x)$ will be normalized to 1 during the computation.
@@ -229 +202 @@
 given for the model will have no affect, and will be ignored when computing
 the average.  This means that any parameter with an array distribution will
-not be fitable.
-
-.. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
-
-Boltzmann Distribution
-^^^^^^^^^^^^^^^^^^^^^^
-
-The Boltzmann Distribution is defined as
-
-.. math::
-
-    f(x) = \frac{1}{\text{Norm}}
-           \exp\left(-\frac{ | x - \bar x | }{\sigma}\right)
-
-where $\bar x$ is the mean of the distribution and *Norm* is a normalization
-factor which is determined during the numerical calculation.
-The width is defined as
-
-.. math:: \sigma=\frac{k T}{E}
-
-which is the inverse Boltzmann factor,
-where $k$ is the Boltzmann constant, $T$ the temperature in Kelvin and $E$ a
-characteristic energy per particle.
-
-.. figure:: pd_boltzmann.jpg
-
-    Boltzmann distribution.
+not be fittable.
 
 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ

sasmodels/weights.py

@@ -97 +97 @@
         return x, px
 
-class UniformDispersion(Dispersion):
-    r"""
-    Uniform dispersion, with width $\sigma$.
-
-    .. math::
-
-        w = 1
-    """
-    type = "uniform"
-    default = dict(npts=35, width=0, nsigmas=1)
-    def _weights(self, center, sigma, lb, ub):
-        x = self._linspace(center, sigma, lb, ub)
-        x = x[np.fabs(x-center) <= np.fabs(sigma)]
-        return x, np.ones_like(x)
 
 class RectangleDispersion(Dispersion):
@@ -204 +190 @@
         return x, px
 
-class BoltzmannDispersion(Dispersion):
-    r"""
-    Boltzmann dispersion, with $\sigma=k T/E$.
-
-    .. math::
-
-        w = \exp\left( -|x - c|/\sigma\right)
-    """
-    type = "boltzmann"
-    default = dict(npts=35, width=0, nsigmas=3)
-    def _weights(self, center, sigma, lb, ub):
-        x = self._linspace(center, sigma, lb, ub)
-        px = np.exp(-np.abs(x-center) / np.abs(sigma))
-        return x, px
 
 # dispersion name -> disperser lookup table.
@@ -224 +196 @@
 MODELS = OrderedDict((d.type, d) for d in (
     RectangleDispersion,
-    UniformDispersion,
     ArrayDispersion,
     LogNormalDispersion,
     GaussianDispersion,
     SchulzDispersion,
-    BoltzmannDispersion
 ))
 
@@ -276 +246 @@
     import pylab
 
-<<<<<<< HEAD
-    if any(len(dispersity)>1 for value, dispersity, weights in mesh):
-        labels = [p.name for p in model_info.parameters.call_parameters]
-        #pylab.interactive(True)
-        pylab.figure()
-        for (v,x,w), s in zip(mesh, labels):
-=======
     if any(len(dispersity) > 1 for value, dispersity, weights in mesh):
         labels = [p.name for p in model_info.parameters.call_parameters]
@@ -288 +251 @@
         pylab.figure()
         for (v, x, w), s in zip(mesh, labels):
->>>>>>> master
             if len(x) > 1:
                 pylab.plot(x, w, '-o', label=s)